{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "delayed-gambling",
   "metadata": {},
   "source": [
    "# Polish National Identification Numbers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "important-damages",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "former-justice",
   "metadata": {},
   "source": [
    "The function `clean_pl_pesel()` cleans a column containing Polish national identification number (PESEL) strings, and standardizes them in a given format. The function `validate_pl_pesel()` validates either a single PESEL strings, a column of PESEL strings or a DataFrame of PESEL strings, returning `True` if the value is valid, and `False` otherwise."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "opening-warrior",
   "metadata": {},
   "source": [
    "PESEL strings can be converted to the following formats via the `output_format` parameter:\n",
    "\n",
    "* `compact`: only number strings without any seperators or whitespace, like \"44051401359\"\n",
    "* `standard`: PESEL strings with proper whitespace in the proper places. Note that in the case of PESEL, the compact format is the same as the standard one.\n",
    "* `birthdate`: split the date parts from the number and return the birth date, like \"1944-05-14\".\n",
    "* `gender`: get the person's birth gender ('M' or 'F').\n",
    "\n",
    "Invalid parsing is handled with the `errors` parameter:\n",
    "\n",
    "* `coerce` (default): invalid parsing will be set to NaN\n",
    "* `ignore`: invalid parsing will return the input\n",
    "* `raise`: invalid parsing will raise an exception\n",
    "\n",
    "The following sections demonstrate the functionality of `clean_pl_pesel()` and `validate_pl_pesel()`. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "polished-factor",
   "metadata": {},
   "source": [
    "### An example dataset containing PESEL strings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "monetary-liechtenstein",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "df = pd.DataFrame(\n",
    "    {\n",
    "        \"pesel\": [\n",
    "            \"44051401359\",\n",
    "            \"44051401358\",\n",
    "            '7542011030',\n",
    "            '7552A10004', \n",
    "            '8019010008',\n",
    "            \"hello\",\n",
    "            np.nan,\n",
    "            \"NULL\",\n",
    "        ], \n",
    "        \"address\": [\n",
    "            \"123 Pine Ave.\",\n",
    "            \"main st\",\n",
    "            \"1234 west main heights 57033\",\n",
    "            \"apt 1 789 s maple rd manhattan\",\n",
    "            \"robie house, 789 north main street\",\n",
    "            \"1111 S Figueroa St, Los Angeles, CA 90015\",\n",
    "            \"(staples center) 1111 S Figueroa St, Los Angeles\",\n",
    "            \"hello\",\n",
    "        ]\n",
    "    }\n",
    ")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "stone-compact",
   "metadata": {},
   "source": [
    "## 1. Default `clean_pl_pesel`\n",
    "\n",
    "By default, `clean_pl_pesel` will clean pesel strings and output them in the standard format with proper separators."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "possible-breed",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataprep.clean import clean_pl_pesel\n",
    "clean_pl_pesel(df, column = \"pesel\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "moral-phone",
   "metadata": {},
   "source": [
    "## 2. Output formats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "pharmaceutical-release",
   "metadata": {},
   "source": [
    "This section demonstrates the output parameter."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "juvenile-threat",
   "metadata": {},
   "source": [
    "### `standard` (default)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "wound-array",
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_pl_pesel(df, column = \"pesel\", output_format=\"standard\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "grateful-variable",
   "metadata": {},
   "source": [
    "### `compact`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "wooden-thread",
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_pl_pesel(df, column = \"pesel\", output_format=\"compact\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "experienced-domain",
   "metadata": {},
   "source": [
    "### `birthdate`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "quiet-argentina",
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_pl_pesel(df, column = \"pesel\", output_format=\"birthdate\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sorted-helena",
   "metadata": {},
   "source": [
    "### `gender`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "interpreted-ribbon",
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_pl_pesel(df, column = \"pesel\", output_format=\"gender\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "compressed-colonial",
   "metadata": {},
   "source": [
    "## 3. `inplace` parameter\n",
    "\n",
    "This deletes the given column from the returned DataFrame. \n",
    "A new column containing cleaned PESEL strings is added with a title in the format `\"{original title}_clean\"`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "opened-brisbane",
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_pl_pesel(df, column=\"pesel\", inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "floppy-guinea",
   "metadata": {},
   "source": [
    "## 4. `errors` parameter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "norwegian-membrane",
   "metadata": {},
   "source": [
    "### `coerce` (default)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "virtual-accounting",
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_pl_pesel(df, \"pesel\", errors=\"coerce\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "biblical-proportion",
   "metadata": {},
   "source": [
    "### `ignore`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "middle-italian",
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_pl_pesel(df, \"pesel\", errors=\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rubber-teaching",
   "metadata": {},
   "source": [
    "## 4. `validate_pl_pesel()`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "chemical-lender",
   "metadata": {},
   "source": [
    "`validate_pl_pesel()` returns `True` when the input is a valid PESEL. Otherwise it returns `False`.\n",
    "\n",
    "The input of `validate_pl_pesel()` can be a string, a Pandas DataSeries, a Dask DataSeries, a Pandas DataFrame and a dask DataFrame.\n",
    "\n",
    "When the input is a string, a Pandas DataSeries or a Dask DataSeries, user doesn't need to specify a column name to be validated. \n",
    "\n",
    "When the input is a Pandas DataFrame or a dask DataFrame, user can both specify or not specify a column name to be validated. If user specify the column name, `validate_pl_pesel()` only returns the validation result for the specified column. If user doesn't specify the column name, `validate_pl_pesel()` returns the validation result for the whole DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "hungry-remains",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataprep.clean import validate_pl_pesel\n",
    "print(validate_pl_pesel('44051401359'))\n",
    "print(validate_pl_pesel('44051401358'))\n",
    "print(validate_pl_pesel('7542011030'))\n",
    "print(validate_pl_pesel('7552A10004'))\n",
    "print(validate_pl_pesel('8019010008'))\n",
    "print(validate_pl_pesel(\"hello\"))\n",
    "print(validate_pl_pesel(np.nan))\n",
    "print(validate_pl_pesel(\"NULL\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "advised-going",
   "metadata": {},
   "source": [
    "### Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "broad-arthritis",
   "metadata": {},
   "outputs": [],
   "source": [
    "validate_pl_pesel(df[\"pesel\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dimensional-leather",
   "metadata": {},
   "source": [
    "### DataFrame + Specify Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "hollywood-advancement",
   "metadata": {},
   "outputs": [],
   "source": [
    "validate_pl_pesel(df, column=\"pesel\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "august-merchant",
   "metadata": {},
   "source": [
    "### Only DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "animal-athletics",
   "metadata": {},
   "outputs": [],
   "source": [
    "validate_pl_pesel(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "vertical-spending",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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